cuda: grouped-int4 (fmt=4) support in the expert-group kernels (#334)

The grouped MoE kernels were per-row-only: GroupDesc had no group-size
fields, row_bytes() returned 0 for fmt=4, the scale buffer was hardcoded
to O floats, and a fmt=4 group that reached the generic path would have
been silently decoded as int2. This closed the GPU expert tier to every
grouped container — including the g64 quality line (#225) and the E8
lattice route (#347) whose whole point is fitting more experts in VRAM.

- ColiCudaTensor gains gs/scale_count; upload allocates O*ceil(I/gs)
  scales for fmt=4 and applies the same offset->signed nibble conversion
  as fmt=2 (identical packing). New ABI entry coli_cuda_tensor_upload_g
  carries gs without touching the existing symbol — an old Windows DLL
  missing it returns 0 and the tensor simply stays CPU-side.
- GroupDesc gains per-tensor group sizes; new grouped_hidden_g4_dual /
  grouped_down_g4 apply the per-group scale inside the accumulation
  (gs is required even, so a packed byte never straddles groups; gs=0
  degrades to per-row, letting fmt=2 members ride the same launch).
- coli_cuda_expert_group routes any group containing fmt=4 through the
  g4 kernels; pure-fmt=2 groups keep the existing paths byte-identical.
  The generic fallback now explicitly rejects fmt=4 instead of decoding
  garbage (#334's prevention note, made real).

tests/test_grouped_g4_cuda.cu: kernel-vs-CPU oracle over 50 trials x 3
experts — gs=64, a non-divisible tail group (200 % 64), and a per-row
member in the same launch: zero mismatches on a 5090.

make check 77/77; CPU, CUDA and MinGW builds clean.

c/backend_cuda.h

@@ -36,6 +36,9 @@ COLI_CUDA_DLLEXPORT void coli_cuda_group_stats(uint64_t *calls, uint64_t *expert
                            double *h2d_ms, double *kernel_ms, double *d2h_ms);
 
 /* Upload without executing, so capacity failures happen during model startup. */
+COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor,
+        const void *weights, const float *scales,
+        int fmt, int I, int O, int device, int gs);
 COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload(ColiCudaTensor **tensor,
                             const void *weights, const float *scales,
                             int fmt, int I, int O, int device);